Finally, Hermes Agent's Desktop App is Here!
CopilotKit is an open-source framework designed to help developers build AI applications with interactive interfaces similar to Claude Artifacts. It utilizes the AG-UI protocol to decouple the agent backend from the frontend, allowing for features like generative UI, real-time state synchronization, and persistent session history. By providing pre-implemented React components, CopilotKit simplifies the process of integrating agent frameworks like LangGraph and CrewAI into full-stack applications. This approach standardizes agent-to-user communication, making it easier to build complex, interactive AI experiences without engineering the interface layer from scratch. The Hermes Desktop App is a platform for running and managing AI agents locally on a computer. It features a three-tier memory system and supports custom Model Context Protocol (MCP) servers for expanded functionality. The app also enables multi-agent coordination through Hermes Kanban and integrates with external services like Telegram. Feature scaling methods like MinMaxScaler and Standardization are essential for adjusting feature ranges to prevent specific variables from dominating model outputs. However, these techniques do not alter the underlying statistical distribution of the data; for example, skewed data remains skewed after scaling. To change a distribution's shape, such as reducing skewness, feature transformations like Log, Sqrt, or Box-Cox transforms must be used instead.
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Build your own Claude app with open-source tooling!
CopilotKit is an open-source framework designed to help developers build AI applications with interactive interfaces similar to Claude Artifacts. It utilizes the AG-UI protocol to decouple the agent backend from the frontend, allowing for features like generative UI, real-time state synchronization, and persistent session history. By providing pre-implemented React components, CopilotKit simplifies the process of integrating agent frameworks like LangGraph and CrewAI into full-stack applications. This approach standardizes agent-to-user communication, making it easier to build complex, interactive AI experiences without engineering the interface layer from scratch.
- Claude Artifacts allow agents to render interactive components like charts and dashboards directly in the UI.
- Standard agent frameworks like LangGraph and CrewAI do not natively include the interface layer required for generative UI.
- CopilotKit is an open-source framework with over 30,000 GitHub stars that provides infrastructure for building full-stack agentic apps.
- The AG-UI protocol serves as a standard for communication between agents and user interfaces, ensuring frontend-backend compatibility.
- CopilotKit includes features such as human-in-the-loop approvals, shared state, and persistent threads for session storage.
- The framework supports a self-learning layer by capturing interaction history to improve agent performance over time.
Finally, Hermes agent's desktop app is here!
The Hermes Desktop App is a platform for running and managing AI agents locally on a computer. It features a three-tier memory system and supports custom Model Context Protocol (MCP) servers for expanded functionality. The app also enables multi-agent coordination through Hermes Kanban and integrates with external services like Telegram.
- The Hermes Desktop App allows users to run AI agents locally with customizable models and providers.
- The system implements a three-tier architecture to manage agent memory and context.
- Users can extend agent capabilities by adding custom MCP servers and utilizing the Skills Hub.
- The platform supports multi-agent workflows using profiles, personas, and the Hermes Kanban system.
- Agents can be integrated with Telegram for external communication and interaction.
- The underlying Hermes Agent technology utilizes GEPA optimization and self-evolving skills.
What feature scaling is not used for?
Feature scaling methods like MinMaxScaler and Standardization are essential for adjusting feature ranges to prevent specific variables from dominating model outputs. However, these techniques do not alter the underlying statistical distribution of the data; for example, skewed data remains skewed after scaling. To change a distribution's shape, such as reducing skewness, feature transformations like Log, Sqrt, or Box-Cox transforms must be used instead.
- MinMaxScaler rescales features to a range between 0 and 1.
- Standardization adjusts features to have a mean of zero and a standard deviation of one.
- Feature scaling and standardization do not change the underlying distribution of the data.
- Scaling ensures models are robust to wide variations in data magnitude but does not eliminate skewness.
- Feature transformations like Log, Sqrt, and Box-Cox are specifically used to transform skewed data into a normal distribution.